35 skills found · Page 1 of 2
haltcase / Param.macroPartial application syntax and lambda parameters for JavaScript, inspired by Scala's `_` & Kotlin's `it`
oguzhan-yilmaz / Aws Lambda Scheduleraws-lambda-scheduler is EventBridge Rule manager that lets you call any existing AWS Lambda Function you have in a set future time with pre-set parameters. Allows more rule creation than AWS limit.
haltcase / Babel Plugin Partial Application[DEPRECATED] Please use https://github.com/citycide/param.macro
IRCraziestTaxi / Ts Simple NameofParses a dot-separated property name from a lambda expression and provides some level of type safety using type parameters.
forward3d / AlpinistAutomatic Alpine Linux Package (apk) Repository Generation using AWS Lambda, S3 & SSM Parameter Store
awslabs / Custom Lookup LambdaCloudFormation Custom Resource can be leveraged to query AWS DynamoDB via an AWS Lambda function to retrieve the key value pairs, replacing the mappings and parameter sections and providing a more automated approach for managing template parameters.
kvasilaky / InverseProblemThis function inverts ill conditioned matrices using an iterative solution to the Tikhonov regularization problem. It takes three arguments: A, the matrix, l, lambda the contraint, and k, the number of iterations. In this iterative Tikhonov regularization model, also known as ridge regression, I introduce an iterative solution to the ill-posed linear inverse problem. My approach to the inverse problem can be viewed as a generalization of existing methods, where, in addition to the regularization parameter, I introduce a second regularization parameter as the number if iterations. This work is motivated by the fact that the least squares solution does not give a reasonable result when the data matrix is singular or ill-conditioned. Test cases show that the approach is either better or significantly better than existing L2 regularization methods.
tallpants / Ssm Parameter Storeλ✨ Ergonomic SSM Parameter Store wrapper for AWS Lambda
aws-samples / Amazon Rekognition Custom Labels A2i Automated Continuous Model ImprovementWith Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.
ssr-diaries / Development Of Autonomous Downscaled Model Car Using Neural Networks And Machine LearningMachine learning using convolution neural network Required: raspberry pi pi cam compatibile rc car motor driver l293d Please create the respective files: forward idle left right reverse optimized_thetas This project aims to build an autonomous rc car using supervised learning of a neural network with a single hidden layer. We have not used any Machine Learning libraries since we wanted to implement the neural network from scratch to understand the concepts better. We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor. Connect the FRONT_MOTOR_DATA_ONE and FRONT_MOTOR_DATA_TWO GPIO pins(GPIO19 and GPIO26) of the Raspberry Pi to the Input pins for Motor 2(Input 3, Input 4) in the IC. Connect the Output pins for Motor 2(Output 3, Output 4) of the IC to the front motor. The PWM_FREQUENCY and INITIAL_PWM_DUTY_CYCLE represent the initial frequency and duty cycle of the PWM output. We have created five class labels namely forward, reverse, left, right and idle and assigned their expected values. All class labels would require a folder of the same name to be present in the current directory. The input images resize to the dimension of the IMAGE_DIMENSION tuple value during training. The LAMBDA and HIDDEN_LAYER_SIZE values represent the default lambda value and the number of nodes in the hidden layer while training the neural network. All these values are configurable in configuration.py. The images for training are captured using interactive_control_train.py, the car is controlled using the direction arrows and all the images are recorded in the same folder along with the corresponding key press. After segregating the images into their corresponding class folders, the neural network is trained using train.py which takes two optional arguments - lambda and hidden layer size; default values would be those specified in the configuration file. At the command prompt, run the following command Once we have the trained model, the RC car is run autonomously using autonomous.py which takes an optional argument for the trained model; default will use the latest model in the optimized_thetas folder. Please feel free to post your doubts on code through my linkedin link: edin.com/in/shreyas-ramachandran-srinivasan-565638117/ CONTROLLING THE CAR The controlling process consists of 4 parts: The sensor interface layer includes various programming modules worried about getting and time stamping all sensor information. The discernment layer maps sensor information into inward models. The essential module in this layer is the PI camera, which decides the vehicle's introduction and area. Two distinct modules enable auto to explore in view of ultrasonic sensor and the camera. A street discovering module utilizes the PI camera determined pictures to discover the limit of a street, so the vehicle can focus itself along the side. At last, a surface evaluation module separates parameters of the present street to determine safe vehicle speeds. The control layer is in charge of managing the controlling, throttle, and brake reaction of the vehicle. A key module is the way organizer, which sets the direction of the vehicle in controlling and speed space. The vehicle interface layer fills in as the interface to the robot's drive-by-wire framework. It contains all interfaces to the vehicle's brakes, throttle, and controlling wheel. It likewise includes the interface to the vehicle's server, a circuit that manages the physical capacity to a significant number of the framework segments. In the proposed system, the raspberry Pi is used to control the L293D board, which allows motors to be controlled through the raspberry pi through the pulses provided by it. Based on the images obtained, raspberry pi provides PWM pulses tocontrol the L293D controller. L293D is a 16 Pin Motor Driver IC as shown in Figure 9. This is designed to provide bidirectional drive currents at voltages from 5 V to 36 V. Fig 9 L293D Breakout Board It also allows the speed of the motor to be controlled using PWM. It’s a series of high and low. The Duration of high and low determine the voltage supplied to the motor and hence the speed of the motor. PWM Signals: The DC motor speed all in all is specifically relative to the supply voltage, so if lessen the voltage from 9 volts to 4.5 volts, then our speed turn out to be half of what it initially had. Yet, for changing the speed of a dc motor we can't continue changing the supply voltage constantly. The speed controller PWM for a DC motor works by changing the normal voltage provided to the motor.The input signals we have given to PWM controller may be a simple or computerized motion as per the outline of the PWM controller. The PWM controller acknowledges the control flag and modifies the obligation cycle of the PWM motion as indicated by the prerequisites. In these waves frequency is same but the ON and OFF times are different. Recharge power bank of any capacity, here, 2800 mAH is used (operating voltage of 5V DC), can be used to provide supply to central microcontroller. The microcontroller used will separate and supply the required amount of power to each hardware component. This battery power pack is rechargeable and can get charged and used again and again.
aws-samples / Parameters Secrets Lambda Extension SampleNo description available
druids / Terraform Aws Asg Instance RefreshTerraform module providing a simple AWS Lambda function to update Launch Template for Auto Scaling Group to use the latest AMI stored in SSM Parameter.
eduardo3g / Terraform ServerlessCreate immutable infrastructure with IaC technologies at AWS with Terraform and Serverless Framework ☁️ The main services used are DynamoDB, SNS, SQS, Lambda, API Gateway, and SSM Parameter Store.
kultgestalt666 / AWSLeakBuster🔍 Scans AWS accounts for potentially sensitive data leaks (SSM parameters, Lambda env vars, Secrets Manager, EC2 UserData, etc.). Not a professional developer – feel free to contribute!
jakejscott / Rust Parameters Lambda ExtensionA lambda extension to hot reload parameters from SSM Parameter Store, Secrets Manager, DynamoDB, AppConfig
alexsobolev / Kokoro Tts KotlinProduction ready text to speech service built with Kotlin, Kokoro (82M parameters), ONNX Runtime, and Clean Architecture. REST API + MCP server for AI assistants. Multi speaker dialogue, POS aware phonemization, AWS deployment (EC2/Lambda).
Hexstream / Positional Lambdapositional-lambda is a concise, intuitive and flexible syntax (macro) for trivial lambdas that eschews explicit (and often contextually-redundant) naming of parameter variables in favor of positional references, with support for a used or ignored &rest parameter and automatic declaration of ignored parameters when logical "gaps" are left in the positional references. Further convenience features are provided.
abdullahkhawer / Aws Ssm Parameter Store BackupA Terraform module to create AWS resources which are used to automatically take backup of all the parameters residing on AWS SSM Parameter Store in JSON format and store it on AWS S3 bucket using AWS Lambda function based on Python. It is executed daily via AWS CloudWatch or AWS EventBridge.
ziedbentahar / Aws Parameters And Secrets Lambda Extension SampleNo description available
docker-production-aws / Lambda Secrets ProvisionerAWS Lambda Function for provisioning secrets into the EC2 Systems Manager Parameter Store
